Executive Education: Competing at Speed in a Fast Moving World

 June 19, 2015 Making the Complex Simple: The gold standard of success at MIT Steven J. Spear DBA MS MS Senior Lecturer, MIT MIT exists for a particular purpose, to solve societies’ problems, and in a particular fashion1: through the combination of thinking up ideas, testing them in the lab or otherwise in practice, and using that tangible experience to inform better thinking. Figure 2: Mens et manus: Minds and hands.2 Figure 1: Lobby 7 The gold standard at MIT is taking phenomena that previously had been viewed as being intractably complex and finding the fundamental principles of causality that control that phenomena. In doing so, the latitude of action increases exponentially in using those fundamentals to good effect. This is evident in the latitude post Newton designers had relative to pre Newtonian ones (e.g., Eiffel and Gehry compared to designers in Medieval Europe). 1
Established for Advancement and Development of Science its Application to Industry the Arts Agriculture and Commerce. 2
Shouldn’t they be looking at each other, even shaking hands? [email protected] -­‐ 1 -­‐ [email protected] June 19, 2015 It is evident in managerial disciplines like finance, where excellence is not “the art of the trade” but the creation of complex trading programs that can find and exploit micro arbitrages across instruments and markets. Figure 3: Pre-­‐Newtonian views of heavenly body motion3 Figure 4: Simple theory of objects movement 3
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1925.jpg [email protected] -­‐ 2 -­‐ [email protected] June 19, 2015 Figure 5: Design (non) latitude before Newtonian mechanics Figure 6: Design latitude with Newtonian mechanics [email protected] -­‐ 3 -­‐ [email protected] June 19, 2015 Figure 7: Design latitude with Newtonian mechanics + high speed computation Figure 8: Finance made simple: Payment timing matters, decision timing matters, diversify risk [email protected] -­‐ 4 -­‐ [email protected] June 19, 2015 Competing at Speed in a Fast Moving World: Getting to the right answer fastest Steven J. Spear DBA MS MS Senior Lecturer, MIT Certain organizations yield enormous rewards for being just enough ahead of their competitors in bringing new ideas to market in the forms of products and services that are just enough better to reap outsize rewards. Figure 1: To the winners go the spoils For instance, Toyota fights with Ford and GM with products in exactly the same segments in exactly the same region. However, being just enough ahead on features, functionality, reliability, and efficiency of delivery means that Toyota has a nearly $3,000 profit per unit, whereas next best doesn’t cross the $1,000 per unit threshold. Apple makes computers and mobile devices similar to those of its rivals, but it is just enough ahead on function and features for the products and its supporting eco system that they struggle whereas Apple is one of the highest valued companies in the world. Likewise, the whole semiconductor industry is compelled to make devices smaller, faster, and more energy efficient. Only Intel has subscribed to Moore’s Law of doubling speed every 18-­‐24 months. Other organizations, across an enormous range of product, process, market, core science, talent pool, etc. have been able to accelerate their ability to develop, design, and deliver value to market as speed. Enormous benefit accrues to all stakeholders. [email protected] -­‐ 1 -­‐ [email protected] June 19, 2015 Key questions: • Key value proposition of your business process from its customers/clients • Key metrics of success and current scores • Key metrics of success and scores necessary to be the world’s best Figure 2: Acceleration in high tech production and design Figure 3: Impeccable safety, quality, and productivity in heavy industry Figure 4: Liberating smart people to do high value adding work Figure 5: From high risk to matter of fact provision of care Figure 6: From “if everything works right” to safety at speed [email protected] -­‐ 2 -­‐ [email protected] June 19, 2015 The Learning Engine Steven J. Spear DBA MS MS Senior Lecturer, MIT Background: Accelerated Learning Key to Elevated Performance Select organizations bring value to market, so much more and so much faster than their rivals, and they reap outsize prizes for doing so. Think the disproportionate rewards to Intel for adhering to Moore’s Law when others raced to keep up; to Apple for consistently being ahead of the pack in the latest permutations on smart-­‐phones, tablets and the eco systems in which they are embedded; or to Toyota which has put hybrid drive on 20+ platforms and sold more than 7 million copies whereas GM has just the Chevy Volt with some 90 thousand in sales volume. That some do so much more so much more quickly is a direct consequence of being able to outlearn their rivals, both in terms of what to do to delight the market and how to do it most efficiently and effectively. While simply saying superior ‘learning’ seems too glib and simple an answer for explaining superior marketplace performance, superior learning is, in fact, the essence of the answer. After all, if you and I both do the same work, but you are much better, it must be because you know a whole lot more than I Figure 1 do about how to put to great effect the resources you have, and if you know a lot more today, it is because yesterday and everyday before that you learned more than I did. In other words, performance is a proxy for accumulated knowledge/know how, and knowledge is ‘the area under the learning curve.’ The outsize rewards are not limited just to those standouts like Intel, Apple, and Toyota, all of which have led for decades. Other organizations have learned to manage the systems for which they are responsible, so they too bring value to market at speed in completely outsize ways, enjoying outsize rewards for doing so. For instance: • High Tech Manufacturing: A microchip plant compressed from 60 days to 24 throughput time. Moving from typical to world’s fastest for its product type (plus cutting unit costs in half) added $10 million/month in additional profit. • Unionized Utility: DTE Energy added velocity to its field service operations, doubling productivity, and then turned to core power generation efforts, cutting operating costs by 25%, a perfectly timed buffer when GM and Chrysler declared bankruptcy. [email protected] -­‐ 1 -­‐ [email protected] •
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June 19, 2015 High Tech Product Design: Pratt and Whitney accelerated product development time from 4 years to 3, cut engineering changes in half (an indicator both of cost and design quality), winning the Joint Strike Fighter F-­‐35 program, the largest weapons program in history. Large scale healthcare system: Hospitals in Pittsburgh cut the rate of hospital related complications by 70%. Individual hospitals completely eliminated particular complications. The emergency department at UPMC cut from over an hour to 18 minutes the time from sign in to orders being written by a physician.1 Back office operations: Alcoa compressed cycle time for monthly closing of the books from two weeks to two days. Liberated resources were able to support operational improvement efforts on safety, quality, yield, and so forth, that, among other things, made Alcoa the safest employer in the USA, with a risk of ‘reportable’ injury 0.07% when the national risk was 2%. In the 1990s, Alcoa stock price behaved like a pre bust doc.com, not an industrial. Public Sector: The US Treasury compressed closing the government’s books from six months each year to 3 days. “Liberated” analytical talent was used to capture real time data for retail sales, industrial production, credit card activity, and so forth. Post 9/11, this meant Secretary of Treasury Paul O'Neill and Chairman of the Federal Reserve Alan Greenspan were making fiscal and monetary policy based on real, current data, not speculative. The Question The question is, what do organizations do to accelerate learning, leading to superior knowledge, leading superlative performance? The Solution The answer is that the standouts create “a learning engine” that powers them to new insights about objectives and approaches. The key elements to this learning engine are: • Seeing problems: Designing all work so problems can be seen when and where they occur. • Solving problems: When problems are seen, swarming them immediately to prevent their spread and start their investigation and treatment. • Sustaining/Spreading learnings: The true standouts don’t leave local discoveries local (in time or place). They make sure that what’s new is sustained locally and spread broadly. 1
With 75% of patients discharged and 10% admitted within 18 minutes, the unit went from crowded and oppressive for patients and staff to calm and tranquil for compassionate care. A ‘gross pathology’ laboratory accelerated turnaround times from days to hours on breast biopsies. A nutrition and dietary department converted from ‘slinging hash’ to being integral to assessment and treatment. Tracking what patients actually ordered and ate relative to what was recommended was a trigger for additional education and prescription. [email protected] -­‐ 2 -­‐ [email protected] June 19, 2015 Figure 2: Poor see and solve Figure 3: Great see solve and share Figure 4: Good see and solve, no share Critically, creating, sustaining, and engaging a see, solve, share dynamic cannot be delegated. It is the inalienable obligation of leaders. Why? Myriad psycho-­‐social pressures and biases encourage us to call out only what is going right and preclude us from calling attention to errors, omissions, inattention, slips, mistakes, and other evidence of imperfect competency. Even if we call out what we perceive as being wrong, often we focus on what is most pronounced by timing, severity, or drama, leveling over myriad other reasons to be concerned about current conditions. If leaders don’t constantly encourage/insist on superlative learning behavior of actively seeking aberration and other contradictions to accepted wisdom, the culture will regress, and the discovery will dissipate. [email protected] -­‐ 3 -­‐ [email protected] June 19, 2015 Converting All Action into Feedback Generating Experiments Characteristic of high velocity organizations is the conversion of all work into a series of high-­‐
speed hypothesis testing experiments. First is an example from the Navy’s experience developing a deploying nuclear power (extracted from Chapter 5 of The High Velocity Edge). Then there is a generalization of these ideas. This discipline of specifying expectations was not just for the frontline work of operating the submarines. It applied to everything, and Rickover himself modeled this way of life. Rockwell recalls preparing for a meeting and being challenged by Rickover to describe how the meeting would conclude— before it had even started. Rickover was not hazing him and did not expect him to be clairvoyant. Rather, he wanted Rockwell to predict in advance what a successful outcome would look like and how he expected to get there so he could determine whether something was amiss as the meeting proceeded. What was there about the situation, the discussion, the technical content, or the discussants that he had misunderstood? What were the consequences of that misunderstanding? What had to be done to address those misunderstandings? Those were all critical concerns, which otherwise might have been missed had Rockwell not been prepared to be surprised by events unfolding contrary to what he had anticipated. Even—and especially—in upfront design and development work, where there were obviously great gaps in what was known about a particularly complex situation, this discipline was required. Rockwell describes designing the radiation shielding for reactors (a topic on which he became expert enough to author several books). No one knew how neutron bombardment would fatigue the metal and how the piping’s welds, joints, and bends would affect radiation patterns. Therefore, when it was time to test the shielding, a map of the entire surface was made, with sensors distributed all across it. But the evaluation didn’t rest at that. Before any measurements were taken, Rockwell insisted that predictions be made about what the measurements at each point would be. It was not sufficient to find out if the various sections passed or failed in terms of emitted radiation. Rockwell and his colleagues already knew that they would be wrong at many points since the science and technology were still in early stages. Therefore, they wanted to know for certain—sooner rather than later—exactly where and when they were wrong and what they misunderstood. The sensors were not just there to mark safe and unsafe situations. They were there to identify pockets of ignorance on the part of the shielding designers. That is why, rather than just recording readings and noting where the exposure was too high, they first predicted what the readings would be, and they compared those to the actual readings to discover where their understanding was confirmed and where it was refuted. If the shielding worked less well than needed or expected, that certainly [email protected] -­‐ 4 -­‐ [email protected] June 19, 2015 warranted investigation and additional engineering. We would all recognize that. However, even if the shielding worked better than needed or expected, that, too, revealed a gap in their knowledge which could prove costly or dangerous and which needed to be plugged. It is not clear we would all see that as a learning necessity as well. The difference? Many tests are meant to distinguish good from bad. In this case, Rockwell structured the test to distinguish understood from not understood. The general point is that declaring what you expect to happen makes what actually happens be a source of surprise that challenges help assumptions/hypotheses and triggers renewed problem solving. This converts all action from “succeed or fail” to “succeed or learn to succeed.” Figure 5: Creating opportunity to be surprised
Figure 6: Succeed or fail Figure 7: Succeed or learn to succeed [email protected] -­‐ 5 -­‐ [email protected] June 19, 2015 Figure 8: Captain of the learning engine and its results Repeatedly creating “predictive loops” is Figure 9: Portfolio of candidates. particularly relevant in drug development and other situations where multiple candidates are being considered, tested, or trialed simultaneously. It involves •
Stating Hypotheses: Declaring in advance of actomg what is expected and why. •
Hypothesis testing: During action, close monitoring for gaps between prediction and actuality. •
Gap Investigation: Immediate Figure 10: Feedback Feed forward characterization of actual situation versus predicted one, with rigorous critique of all theory, models, calculations, and tests that informed hypotheses. •
Hypothesis correction: Use new found understanding to at least explain how actual situation was generated. The conventional approach is to launch ‘candidates’ into a system, hoping some will [email protected] -­‐ 6 -­‐ [email protected] June 19, 2015 succeed and discarding the ones that fail when they filter out. There is a contrasting possibility, if all actions are preceded by creating the possibility to be surprised. With expectations declared, candidates that ‘fault out’ aren’t failures; feedback on their experience can now be feed forward for how to manage other candidates, increasing the chance for them to be successful. Networking this feedback/feed forward across a large portfolio of candidates can be a great game changer. Getting to the Right Anwer First With about one minute left in 2015’s Super Bowl, the Seattle Seahawks seemed to have locked up victory. Passing from the Patriots 38, Seahawks quarterback Russell Wilson launched a deep throw down the right side line. Though, for all appearances, it appeared that Patriot linebacker Jamie Collins (91) had broken up the play, freak bounces and great reflexes by Seahawks receiver Jimmy Kearse (91) turned a game ender into a 35 yard gain, which was followed by a 4 yard run by Seahawks Marshawn Lynch on the next play. For New England fans, this seemed to be premonition for another last minute catastrophic snatching of defeat from the jaws of victory, like weak-­‐hitting Bucky Dent’s 1978 three run homerun that sent the New York Yankees to the World Series, not the Red Sox. Figure 11: Bucky Dent again #&*@?! [email protected] -­‐ 7 -­‐ [email protected] June 19, 2015 One minute later, there was a profound change in fortunes. With two yards to go, Seahawks receiver Ricardo Lockette lined up wide right, broke to the center of the field, ready to catch Russell Wilson’s pass and score the clinching touchdown. Instead, Patriots defender Malcolm Butter got there first, intercepted, and sealed victory that moments before had seemed kaput. Figure 12: Getting to the right answer (place) first That begs the question, how did Butler, a rookie with no previous professional interceptions know where to go and how did he get their first? The knee jerk answers are appealing but all wrong. Was he just that much faster than Lockette? Unlikely, given how talented Lockette must be to play in the pros. Did he somehow ‘intuit’ what was going to happen and what to do? Again, unlikely. This would have required an impossible problem solving speed, given that Lockette already knew the play and where to go. To answer the question, we have to shift the focus from Butler, the athlete, to Bill Belichiick, the coach, and the learning engine he captained—the see, solve, sustain leader as teacher dynamic. Like every football team, the Patriots coaches and players undoubtedly spend enormous time studying game film, both theirs and their opponents, to see problems they’ve experienced and problems that they are likely to face. They also undoubtedly spend time generating new plays towards solving the problems they anticipate seeing. [email protected] -­‐ 8 -­‐ [email protected] June 19, 2015 When it gets to the sustaining/sharing of new knowledge, the Patriots may get somewhat an edge. The Wall Street Journal2 described the relentlessness and creativity with which Belichick practices new offensive and defensive schemes. The Patriots could practice against their teammates, defense playing against the offence and offense helping the defense hone their skills. The problem with that approach is the Patriots would get good at playing . . . the Patriots. Instead, Belichick assembles Figure 13: Coach Belichick—Seeing, solving, sustaining master temporary players to form a virtual opposition. If the Pats are going to have to defend against a player with a particular physique and skill set, they find a doppelganger to practice against. It’s as if you were practicing against next game’s opponent on the holodeck of the USS Enterprise. The Wall Street Journal3 also reported that the constant testing and prodding of plays and players end, describing a “pop quiz” environment of Belichick and his staff ‘cold calling’ players on what situations to expect at different points in a game. It is non-­‐stop rehearsal of new found approaches, both mentally and physically. Finally, there is the interaction the coach has with his players. The stereotypical image of a top level football coach is of dictatorial martinet, issues instructions, enforcing discipline, and otherwise demanding compliance with his ‘vision’ of how things should operate. Not so with Belichick. An NFL Productions film4 captures Belichick coaching rookie Butler on how to react when Seattle lines up with that particular play. “Slightly to the right so you have a jump on the ball.” “Slightly back from the line, so you don’t get blocked.” “Leaning into the direction you’ll have to move.” “Toes pointed to the side, not straight ahead to enhance your jump.” It was reported, Butler practiced <dozens?> of times how to respond to that particular play. That is not exactly right. With Bill Belichick as leader-­‐learner-­‐teacher, he improved on how to respond to that play, so when it counted, he got it right. 2
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“Why Belichick Really Is a Mad Scientist,” WSJ, by Jonathan Clegg and Kevin Clark, Jan. 15, 2014 “Bill Belichick: The NFL’s Scary Alex Trebek,” WSJ, Kevin Clark and Dan Barbarisi, Jan. 14, 2015. Three Games to Glory IV, NFL Productions, 2015. [email protected] -­‐ 9 -­‐ [email protected] June 30, 2015 Competing at Speed in a Fast Moving World: Tactics for getting to the right answer fastest Steven J. Spear DBA MS MS Senior Lecturer, MIT The Reality Certain organizations “bring more value to the fight” than their counterparts, even though they are working off the same resource base. The difference? The standouts “get to the right answer first” in terms of understanding what needs have to be answered, what offerings will do the trick, and what operational configuration will make development, design, and delivery successful. The Challenge The question is, what do other organizations have to do to have accelerated learning, leading to excessive knowledge, enabling superlative performance? The Solution Getting to the right answer first depends on having tactics that allow accelerated learning and discovery. Some of the answer is build networks that are dynamic, constantly adjusting to changes in the networks they are engaging. Some of the answer is getting a problem seeing, problem solving capability out “at the tactical edge.” This has the double benefit of addressing problems sooner faster and also of having a ‘sensor’ capability to have faster awareness of emerging problems and their possible solutions. Example: The Changed Operating Environment Stanley McChrystal, et al, in Team of Teams, explain fundamental transitions in the US military’s operating environment, which might be described as moving from ‘mass on mass,’ to ‘force on point,’ to (dynamic) ‘network on network.’ The Cold War faceoff between NATO and the Warsaw Pact was emblematic of what might be framed as a ‘mass on mass’ situation. The East created massive stocks of armaments, so the reaction on the part of the West was also was to create massive stocks of massive armaments. Were a battle actually to occur, the side with more, bigger things would undoubtedly win. 1 1
A similar model may have dominated strategy in WWI and the lead up to WWII. One can imagine similar confrontations in the commercial world of the 1950s and 1960s—slow moving industrial armadas holding each other at a strategic standoff. [email protected] -­‐ 1 -­‐ [email protected] June 30, 2015 Figure 1: Mass on mass Terrorist actions in the 1970s and 1980s (e.g., Munich Olympics, 1972; the Iranian hostage crisis of 1979-­‐1981 and the failure of the Operation Eagle Claw rescue) persuaded the USA that it had to bring force on particular point threats (with the implication that once force was successfully applied, that particular threat disappeared). Team of Teams, for instance, describes training with elite units configured mechanistically. Navy SEALS disabled radar on a ocean-­‐based oil rig so paratroops could capture an Figure 2: Force on point airfield,allowing special forces to land their equipment and race out to take control of a high jacked aircraft with hostages on board. The commercial analogy for this might be that of a retailer having to create a series of niche offerings, each tailored to a particular market sliver. The medical analogy would be treating cancer as a challenge of excising a tumor (as opposed to the network/system approach of addressing the tumor and the entire genetic and bio systems that allowed it). The situation faced in Iraq, battling Al-­‐Qaeda, Figure 3: AQI Network vs. J-­‐SOC Network was at first perceived as being a series of force on point problems, with special operations units deployed pin point by pin point by pin point. Yet, despite superior training and equipping, and a mission which morally dwarfed that of the opposition, Al-­‐Qaeda in Iraq was progressing, not losing. The realization by the allied forces was that they were hitting individual ‘points,’ but these points were webbed together in a linked (dynamic) network that kept adapting. [email protected] -­‐ 2 -­‐ [email protected] June 30, 2015 The dynamic part is that every time force was brought on a point, the network morphed, so the next time, the adversary was a little different by operational focus and distribution of resources. To deal with this protean network, the US’s Joint Special Operations Command had to take its disparate parts, piece them together, and establish a much faster tempo of adaptation. In one example of this transformation, Team of Teams describes the initial condition. Soldiers would conduct a raid at night, carry away bags filled with CDs, computer hard drives, and other information, only to have that trove sit unexplored and unexploited in a closet. In effect, the habits of ‘force on point’—combining assets in a mechanistic fashion, using the mechanism once, then essentially dissembling the mechanism into its components until they’re next needed. Figure 4: Joint Special Operations Command Organizational Chart The “J-­‐SOC’” (jay-­‐SOCK) dynamic network was a much changed approach. Raiders might return with a similar haul of data laden materials, but, rather than dump them in storage, they would be immediately run to an analyst who would make as much sense as possible from them. While the ‘operators’ rested, the ‘system’ was developing a much improved image of the enemy, where its resources were, where its targets might be, so on the next night of missions, teams were not acting in a responsive fashion, they were acting in an anticipatory fashion.2 2
While framed in terms of J-­‐SOC’s experience as a military organization in a war zone, the problem of managing distributed networks that are in contest with other distributed networks is hardly unique. Hospitality, once might have been treated as a ‘force on point’ problem—
building and operating a hotel tailored to a specific location and cliental. Now, hospitality chains run a dozen or more brands—Marriott has Bulgari, Ritz Carlton, JW Marriott, Edition, Autograph, Renaissance, AC Hotels, Marriott, Courtyard, Springhill, Fairfield, Residence, and Gaylord, linked by common loyalty program and sales channels competing against networks run by Hilton (Curio, Doubletree, Hilton, Embassy, Hampton, Garden Inn, Homewood, Home2), Hyatt, and others. Changes in price, tie-­‐ins with airlines and rental car agencies, promotional events, and other initiatives, cause a reshaping of one network that has to be anticipated or at least perceived and addressed by the others. Financial institutions too have expanded regionally, and by product and customer type, so it is no longer one bank staking out a claim on its locality, its financial network competing against network, each acting and changing to gain local and systemic advantage. And retail is being transformed by “fast retailing,” characterized by identifying trends, developing [email protected] -­‐ 3 -­‐ [email protected] June 30, 2015 Sensing at the Edge Waging a network on network conflict meant Figure 5: Small problems needing immediate that equipping had to be cone at an solution appropriate scale and scope. The US Army supports several major weapons programs—
the Abrams M-­‐1 Tank, the Apache helicopter, the Bradley Fighting Vehicle. All are large in number, large in scale, and exceptionally long in development—decades for each. The problem is, equipping on such large scale, with such long lead times didn’t fit the operational tempo or scale of Iraq and Afghanistan For instance, how do you solve for the problem of the trooper, operating as part of a small unit, who has to find out what hazards might be hidden in a cave, now, rather than for how to counter the multi-­‐decade Soviet armored threat? The solution was to create the Rapid Equipping Force (the “REF”) with the specific mission of closing the time and space gap between real time problems and off the shelf capabilities that might be configured into an effective solution. Figure 6: Closing problem-­‐space, solution-­‐space gap product, and delivering merchandise within weeks, whereas the traditional retailers are operating on a seasonal cycle months out of sync with current trends. [email protected] -­‐ 4 -­‐ [email protected] June 30, 2015 The REF ethos of getting close to the problem Figure 7: Expeditionary laboratory3 space, tying tightly to the solution space of commercial and governmental ‘off the shelf’ technologies (“COTS” and “GOTS”), so the two could be brought together quickly found representation in the ‘expeditionary laboratory.’ While not all that extraordinary in terms of technology—a freight container stuffed with a 3D printer, CNC machinging, some CAD-­‐CAM capability, and lots of networking capability, the X-­‐Lab was noteworthy by where it was placed and how it was used. The X-­‐lab was pushed out, ‘downrange,’ in theater, close to where ‘troops in contact’ were actually experiencing problems. It was staffed by non-­‐commissioned officers and ‘liaison officers’ who could debrief with soldiers to find out what problems they encountered when conducting missions, on patrol, and so forth. It was also staffed with ‘forward deployed’ engineers and scientists who could quickly design and mockup possible solutions, supported by colleagues and a rich library of other resources. It was not just that the REF was seeing and solving relatively small problems because it was operating on ‘the tactical edge.’ In keeping with the theme of ‘equip, insert, assess,’ these small problems were leading indicators of threats that had potential expression elsewhere, over time. For instance, dealing with threats presented by improvised explosive devices (“IEDs”) or rocket propelled grenades (“RPGs”) in one area meant forewarning about how these threats would presented themselves and how they could be mitigated elsewhere. Causalities related to transport and cargo led to a substantial questioning of why there were so many convoys that left troops at risk. The answer was not to ‘harden’ conveys with tougher vehicles, more of them, and air escort. The answer was to reduce the number by providing soldiers with a more rationalized, hybridized energy system that sharply cut fuel needs. 4 3
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For more details and examples, please see “Equip, Insert, Assess: Soldier driven solutions,” US Army Rapid Equipping Force, accessed on May 25, 2015 from http://www.ref.army.mil/docs/20150330_Rapid_Equipping_Force_Magazine.pdf. [email protected] -­‐ 5 -­‐ [email protected] June 30, 2015 The Small Steps and Giant Leaps In May 1961, President Kennedy challenged his countrymen to “before this decade is out, of landing a man on the moon and returning him safely to the earth.” In July 1969, Neil Armstrong stepped out of the lunar lander, the Eagle, and declared that he had just taken “a small step for man, a giant leap for mankind.” That leads to the rhetorical question, what was the “giant leap?” Figure 8: Challenge and achievement The answer is, there wasn’t one. It was not as if super smart engineers (even MIT trained ones) sat in a design center, banged away at plans, handed those over for fabrication, and some 8 years later, a button was pushed and the mission was ignited. That would have been impossible given all that was unknown but was critical for success. Tackling everything at once would have been two many variables and too few iterations to get anywhere near a successful answer. Figure 9 [email protected] -­‐ 6 -­‐ [email protected] June 30, 2015 Instead, NASA broke the ‘giant leap’ challenge into small leap solvable problems, compartmentalizing problems into the Mercury, Gemini, and Apollo programs. In terms of the lofty goal of to the Figure 10 moon and back, Gemini was un-­‐
ambitious. Single man crews, flights never more than a few hundred miles from the Earth. But that was the point: use each flight to add a few more factors for testing to those that were already known and reasonably well understood. For instance, the first flight took Alan Shepard up and down: no orbit, and no where near to the Moon. It was only the third flight that John Glenn became the first American to orbit the Earth, 3 ½ times. Over subsequent flights flight duration was increased, more orbits, higher altitudes, increasing the understanding of effects of near zero gravity on the pilot, navigation, communication, and so forth. Figure 11 [email protected] -­‐ 7 -­‐ [email protected] June 30, 2015 The Gemini flights moved from one crew to two, allowing flights of even longer duration, practice with work streams in parallel, space walks, rendezvous and docking with other spacecraft, all skills necessary for a flight to the Moon, but all practiced within a few hundred miles of the Earth, not the tens of thousands of miles an actual Moon mission would entail. Transitioning Gemini to Apollo added more incremental learning loops. The addition of a third astronaut to handle a more complex mission task-­‐set, longer duration flights to test equipment and procedures, and finally, on the 8th flight, going all the way to the moon for the first time. Figure 12 When one plots out the various programs that preceded Apollo 11, the height of the rockets is metaphoric for the accumulated knowledge that allowed the accomplishment in 1969 of President Kennedy’s 1961 challenge. Figure 13 [email protected] -­‐ 8 -­‐ [email protected] June 30, 2015 Summary Certain organizations deliver far more value, far faster than their counterparts, displaying attributes of reliability, resilience, and agility otherwise uncharacteristic in their field. Part of the tactics necessary for achieving these characteristics are configuring component elements into a network that has the dynamic capabilities to assess, anticipate, and react quickly, continuously reconfiguring to keep pace with changes in the operating environment. This ability to perceive and react to threats and to begin developing solutions is to put problem seeing capability at the organization’s ‘tactical edge’ and link this tightly to the space of current and emerging capabilities that can be configured into effective solutions. Some may be quickly discarded. Others may prove themselves to have enduring value at larger scale. [email protected] -­‐ 9 -­‐ [email protected]